Artificial intelligence (AI) is expected to have a monumental impact on society. As such, DeepMind, an AI research company now housed under Google parent company Alphabet, has established a new unit dedicated to answering questions about the effect the technology might have on the way we live. DeepMind Ethics and Society will bring together employees from the company and outsiders who are uniquely equipped to offer useful perspectives. Economist and former UN advisor Jeffrey Sachs, University of Oxford AI professor Nick Bostrom, and climate change campaigner Christiana Figueres are among the advisers selected for the group. At present, the unit comprises around eight DeepMind employees and six unpaid fellows from outside the company.

RoboSumo, one of the latest Open AI experiments in machine learning, involves a pair of'robots' dropped into a virtual arena without even the knowledge necessary to walk, and forced to learn the tricks of sumo wrestling purely by trial and error. The video posted on YouTube shows how the bots initially clash without employing any tactics or strategy, but after a number of bouts their movements start to resemble those of human wrestlers, as they learn to dodge and attack. According to the Wired, OpenAI researchers created RoboSumo because the competition apparently generated extra complexity which "could allow faster progress than just giving reinforcement learning software more complex problems to solve alone." "When you interact with other agents you have to adapt; if you don't you'll lose," Maruan Al-Shedivat, one of the RoboSumo creators, said.

With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data. However, to scale the recognition to a large number of classes with few or now training samples for each class remains an unsolved problem. One approach to scaling up the recognition is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/ learning. This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, and from datasets and evaluation settings. We also overview related recognition tasks including one-shot and open set recognition which can be used as natural extensions of zero-shot recognition when limited number of class samples become available or when zero-shot recognition is implemented in a real-world setting. Importantly, we highlight the limitations of existing approaches and point out future research directions in this existing new research area.

Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of the inter-task relationships to identify the relevant knowledge to transfer. These inter-task relationships are typically estimated based on training data for each task, which is inefficient in lifelong learning settings where the goal is to learn each consecutive task rapidly from as little data as possible. To reduce this burden, we develop a lifelong learning method based on coupled dictionary learning that utilizes high-level task descriptions to model the inter-task relationships. We show that using task descriptors improves the performance of the learned task policies, providing both theoretical justification for the benefit and empirical demonstration of the improvement across a variety of learning problems. Given only the descriptor for a new task, the lifelong learner is also able to accurately predict a model for the new task through zero-shot learning using the coupled dictionary, eliminating the need to gather training data before addressing the task.

The announcement by the London-based group acquired by Google parent Alphabet is the latest effort in the tech sector to ease concerns that robotics and artificial intelligence will veer out of human control. "As scientists developing AI technologies, we have a responsibility to conduct and support open research and investigation into the wider implications of our work," said a blog post announcing the launch Tuesday by DeepMind's Verity Harding and Sean Legassick. "At DeepMind, we start from the premise that all AI applications should remain under meaningful human control, and be used for socially beneficial purposes. Understanding what this means in practice requires rigorous scientific inquiry into the most sensitive challenges we face." The post said the focus would be on ensuring "truly beneficial and responsible" uses for artificial intelligence.

DeepMind, the UK's star artificial intelligence company owned by Google, has tripled the amount of money it spends on top talent. Spending on administration largely driven by its wage bill jumped to £164m in 2016, up from £54m a year earlier, according to its newly published annual accounts, as it splashed the cash on attracting and retaining experts in the highly competitive field. "We're really proud that some of the world's most exciting AI research and real-world application is taking place right here in London," said a spokesperson for DeepMind. "We intend to keep investing in our scientific mission, and to work with the world's brightest minds to tackle society's most complex problems." Read more: 5 things we learned about DeepMind's Demis Hassabis on Desert Island Discs The London-based tech firm which was snapped up by Google for £400m in 2014, brought in revenue for the first time last year of £40m from its research and development into AI, the figures also reveal.

Google's artificial intelligence research arm DeepMind has launched a unit focused on ethics and society. The group will conduct and fund research that covers the humanities and social sciences and run public discussion events, DeepMind announced earlier this week. The unit has already released five'core principals' to guide future AI research: that technologies be developed in ways that serve the global social and environmental good; that research be'rigorous and evidence-based' as well as'transparent and open' (including with funding arrangements); that work includes a diversity of voices; and that public opinion will feature in all developments. "This new unit will help us explore and understand the real-world impacts of AI," the group wrote in a blog post earlier this week. "It has a dual aim: to help technologists put ethics into practice, and to help society anticipate and direct the impact of AI so that it works for the benefit of all."

Here's what others have to say about DeepMind Montreal: "DeepMind's exceptional research accomplishments have helped focus the world's attention on AI and to propel new scientific discoveries. The mission of DeepMind, solving intelligence, is perfectly aligned with my own research work and goals. I am really excited to join forces with DeepMind and to help build the new Montreal team. At the same time, I look forward to continue training the next generations of machine learning researchers at MILA and McGill, fostering diversity and inclusion in the research community, through AI projects for social good, and building further the Montreal AI ecosystem." "I am very excited to be working with Doina, Rich Sutton, Mike Bowling, Patrick Pilarski and the rest of our incredible research team to grow DeepMind's research labs in Edmonton and Montreal - two cities with a vibrant AI ecosystem. Key to the health of this ecosystem is the collaboration between academic institutions and industry and I look forward to building strong and enduring ties between the two right here in Canada. I am a big admirer of Doina's work and her focus on AI for social good, which clearly aligns with DeepMind's mission, and I am looking forward to supporting her in her future efforts."

Google acquired the British artificial-intelligence startup DeepMind in 2014 for a reported £400 million (roughly $525 million), a company its cofounder Demis Hassabis once described as aiming at "solving intelligence, and then using that to solve everything else." Since then, the company's researchers have built a system that beat humans at one of the most complicated board games ever, and is now trying to beat humans at complex video games. It's building AI that's learning to navigate 3D spaces as we do, and is training other systems on British medical data, theoretically to spot illness more quickly. It's also started to integrate with teams in the US to bring its work to Google products where they might be useful. All of this research comes at a price.

To understand why WaveNet improves on the current state of the art, it is useful to understand how text-to-speech (TTS) - or speech synthesis - systems work today. The majority of these are based on so-called concatenative TTS, which uses a large database of high-quality recordings, collected from a single voice actor over many hours. These recordings are split into tiny chunks that can then be combined - or concatenated - to form complete utterances as needed. However, these systems can result in unnatural sounding voices and are also difficult to modify because a whole new database needs to be recorded each time a set of changes, such as new emotions or intonations, are needed. To overcome some of these problems, an alternative model known as parametric TTS is sometimes used.